Pro tip: You can use Jev to remove annoying reply guy comments that X seems to always miss. Takes 5 minutes in Astra with the docs and an API key.
API & Models
Token pricing
Jev's published rate: $42 per billion input tokens, with output tokens free.
- Category
- API & Models
- Also known as
- —
- Related terms
- 4
- Directory entries
- 9
- Docs
- docs.typesafe.ai
- Added
- 2026-09-24
Definition
Pricing is charged per input token at $42 per Btok ($0.042 per Mtok). Output tokens are free, so the cost of a request is dominated by how much state and how many questions you send.
TypeSafe's product page frames that as a 238x lower input price than Claude Fable 5.1 and claims 193.6x faster and 444.6x cheaper than LLMs on its own workflows. Those multipliers are vendor-run comparisons rather than independent measurements.
Tagsapipricing
Related terms
Definitions that connect to this one.
Input tokensThe tokens billed on every request: state plus questions, including speculative questions your code ignores.API & ModelsOutput tokensThe tokens Jev emits for answers; they are reported in usage and are free of charge.API & ModelsBenchmark claimsTypeSafe's headline multipliers — 193.6x faster, 444.6x cheaper, a 238x lower input price — come from its own workflow comparisons.Evaluation & TrainingCost/quality frontierPlotting accuracy against cost and time on log scales, where up and to the left is better; cascades are designed to sit there.Evaluation & Training
From the directory
The model card for jev-1.13.0: $42 per billion input tokens with free output, a 64k context, 250k tokens per second, and how to list the models your account can call.
Sites & GuidesDocs#official#docs#models
Official
TypeSafe AI's product site for Jev, its first System One Model: typed decisions with calibrated confidence, performance and pricing claims, a FAQ, and links to the docs, console, workflow evals, and launch post.
Sites & GuidesDocs#official#docs#models
Official
A release guide with eval tables, pricing, curl and Python examples, and fit boundaries for Jev's early access.
Sites & GuidesArticle#community#article#pricing
Community
A use-case directory of 473 Jev builds, 107 guides, and 8 use cases where each entry links to its source and shows the cost and speed its author reported, with category filters and free tools.
Sites & GuidesArticle#community#directory#use-cases
Community
News analysis arguing Jev's refusal to generate text is the feature, covering the $40M seed, the OpenAI-veteran founding team, and what structured, thresholdable decisions mean for agent pipelines.
Sites & GuidesArticle#community#article#models
Community
DataCamp's explainer walks the eval tables behind System One models, from 67.8% workflow accuracy and 0% structured-output errors to the $0.042 per million token price and how to call the API.
Sites & GuidesArticle#community#article#evaluation
Community
3 more matching entries in the full directory.
From the community
Posts from builders shipping with Jev right now.
A filter for reply-guy comments
A Downloads folder that sorts itself
Jev unlocks SO many awesome new ideas. I built a macOS app that monitors my Downloads folder along with a customisable set of rules. Is the downloaded file an invoice? Move it to a special folder with the correct filename. No other LLM calls involved - just Jev!
Which outreach signals book demos
JEV is insanely fast. We gave it a massive dataset based on thousands of outreach messages and asked: Which intent signals generated the most booked demos? 40 seconds later, we had the answer. Cost: less than $0.20. JEV can also rank leads, measure prospect-message fit, and Show more
Website to App turns any URL into a native app
Introducing Jev for 'Website to App' Turn any website into a native mobile app. Just paste a URL. jev-1.13.0 decides how to build the original website as a *native* mobile app, then shipper submits to the app stores for you. We’ve been using this internally a ton for Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
The compaction plugin, in Spanish
Este tipo de herramientas empieza a hacer que el contexto de los agentes parezca menos una ventana limitada y más una memoria que se administra dinámicamente. [fast-jev-compaction] No resume, hace que Jev analice cada tool call y su resultado para decidir qué sigue siendo Show more
A computer assistant that listens
building a computer assistant with Jev local whisper listens to everything I say which then gets classified by Jev to determine what actions to take it uses a small Swift app to provide the full accessibility tree to Jev (i.e. tell its what's on my screen, what can be clicked, Show more
